TidyTuesday or known more commonly as #TidyTuesday is an excellent approach to practice Data Visualization skills using R.

TidyTuesday is a project that was started by Thomas Mock along with the guidance of Jake Kauppe and came into existence by the efforts of the R for Data Science Online Learning Community (R4DS). Every week, on Monday they post a new data set. You can plot the data based on your work and share it along with the code on Twitter using #TidyTuesday.

Purpose of #TidyTuesday

• The data is growing and is random. Yes, it can be ‘tamed’ but putting it in the way it looks more cleaner and representable makes it challenging to handle.

• The main purpose is to emphasize on summarizing and arranging the data through charts and graphs; interpreting its meaning using ggplot2, dplyr, tidyr and other tools in tidyverse. Tidyverse is a collection of R packages designed specifically for data science. It is the platform that provides tools to handle these datasets. Using Tidyverse, one can practice tidytuesday and work on enhancing skills.

• The project was founded to focus on the ways to clean, wrangle, tidy and plot a new dataset which was done every Tuesday.

• Emphasis on projects leads to applying basic R skills, getting feedback, exploring other’s work and gaining new ideas and connecting with the great Rstats community!

What binds an enthusiast with TidyTuesday is building skills with real-world data and acknowledging its distinctiveness. The datasets are open and free. One can even submit a dataset as an Issue if one finds it interesting enough, along with a link and an article.

Tips to make data ‘Tidy’

The 3 rules that make a dataset tidy are:

• Each variable must have its own column.

• Each observation must have its own row.

• Each value must have its own cell.

Submission of Data Sets:

Just follow few simple steps stated here and submit data sets in TidyTuesday

GitHub Reference: TidyTuesday